{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "139002ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penri\\AppData\\Local\\Temp\\ipykernel_23832\\2979793285.py:9: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
      "  set_matplotlib_formats('retina')\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Day</th>\n",
       "      <th>Group</th>\n",
       "      <th>Originality</th>\n",
       "      <th>Interestingness</th>\n",
       "      <th>Writing</th>\n",
       "      <th>Coherence</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Evaluator</th>\n",
       "      <th>unique_id</th>\n",
       "      <th>external</th>\n",
       "      <th>Humanlikeness</th>\n",
       "      <th>English</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Ability</th>\n",
       "      <th>DAT</th>\n",
       "      <th>Total</th>\n",
       "      <th>Idea</th>\n",
       "      <th>Outline</th>\n",
       "      <th>Write</th>\n",
       "      <th>Edit</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Goal</th>\n",
       "      <th>Again</th>\n",
       "      <th>AI_Helpfulness</th>\n",
       "      <th>AI_Satisfaction</th>\n",
       "      <th>AI_Contribution</th>\n",
       "      <th>DAT_Group</th>\n",
       "      <th>Group_</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Human Creativity</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Benjamin Joers</td>\n",
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       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>86.81</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>Human Creativity</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Rio Dharma</td>\n",
       "      <td>1_1</td>\n",
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       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>86.81</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Human Creativity</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>allison liegner</td>\n",
       "      <td>1_2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>86.81</td>\n",
       "      <td>85.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Human Creativity</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Ryan Ho</td>\n",
       "      <td>1_2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>86.81</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Human Confirmation</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Nathan Kidambi</td>\n",
       "      <td>2_1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>Pema Euden</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>87.08</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>5</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <th>2193</th>\n",
       "      <td>295</td>\n",
       "      <td>1</td>\n",
       "      <td>Human Creativity</td>\n",
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       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Alan Wu</td>\n",
       "      <td>295_1</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>74.63</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>295</td>\n",
       "      <td>2</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>295</td>\n",
       "      <td>2</td>\n",
       "      <td>Human Creativity</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Ziqi Yang</td>\n",
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       "      <td>71.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2196 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Day               Group  Originality  Interestingness  Writing  \\\n",
       "0       1    1    Human Creativity            5                3        4   \n",
       "1       1    1    Human Creativity            5                6        4   \n",
       "2       1    2    Human Creativity            2                1        3   \n",
       "3       1    2    Human Creativity            6                7        7   \n",
       "4       2    1  Human Confirmation            6                5        6   \n",
       "...   ...  ...                 ...          ...              ...      ...   \n",
       "2191  294    2             Copilot            6                4        5   \n",
       "2192  295    1    Human Creativity            5                4        4   \n",
       "2193  295    1    Human Creativity            5                5        6   \n",
       "2194  295    2    Human Creativity            5                5        5   \n",
       "2195  295    2    Human Creativity            5                5        7   \n",
       "\n",
       "      Coherence  Overall        Evaluator unique_id  external  Humanlikeness  \\\n",
       "0             7      4.0   Benjamin Joers       1_1         0            4.5   \n",
       "1             5      5.0       Rio Dharma       1_1         0            4.5   \n",
       "2             5      2.0  allison liegner       1_2         0            3.0   \n",
       "3             6      7.0          Ryan Ho       1_2         0            3.0   \n",
       "4             6      6.0   Nathan Kidambi       2_1         0            6.5   \n",
       "...         ...      ...              ...       ...       ...            ...   \n",
       "2191          5      5.0       Pema Euden     294_2         1            3.0   \n",
       "2192          4      4.0       Pema Euden     295_1         1            6.5   \n",
       "2193          5      5.0          Alan Wu     295_1         1            6.5   \n",
       "2194          6      5.0       Pema Euden     295_2         1            3.5   \n",
       "2195          7      6.0        Ziqi Yang     295_2         1            3.5   \n",
       "\n",
       "      English  Experience  Ability    DAT  Total  Idea  Outline  Write  Edit  \\\n",
       "0         3.0         1.0      2.0  86.81   80.0  12.0      8.0   55.0   5.0   \n",
       "1         3.0         1.0      2.0  86.81   80.0  12.0      8.0   55.0   5.0   \n",
       "2         3.0         1.0      2.0  86.81   85.0  10.0     20.0   15.0  40.0   \n",
       "3         3.0         1.0      2.0  86.81   85.0  10.0     20.0   15.0  40.0   \n",
       "4         2.0         1.0      1.0  89.58    NaN  45.0      NaN    NaN   NaN   \n",
       "...       ...         ...      ...    ...    ...   ...      ...    ...   ...   \n",
       "2191      3.0         3.0      2.0  87.08   45.0   3.0      5.0   25.0  12.0   \n",
       "2192      2.0         1.0      1.0  74.63   66.0   1.0      5.0   40.0  20.0   \n",
       "2193      2.0         1.0      1.0  74.63   66.0   1.0      5.0   40.0  20.0   \n",
       "2194      2.0         1.0      1.0  74.63   55.0   5.0     20.0   25.0   5.0   \n",
       "2195      2.0         1.0      1.0  74.63   55.0   5.0     20.0   25.0   5.0   \n",
       "\n",
       "      Satisfaction  Flexibility  Goal  Again  AI_Helpfulness  AI_Satisfaction  \\\n",
       "0              1.0          4.0   2.0    0.0             NaN              NaN   \n",
       "1              1.0          4.0   2.0    0.0             NaN              NaN   \n",
       "2              3.0          3.0   3.0    0.0             4.0              5.0   \n",
       "3              3.0          3.0   3.0    0.0             4.0              5.0   \n",
       "4              2.0          6.0   2.0    0.0             NaN              NaN   \n",
       "...            ...          ...   ...    ...             ...              ...   \n",
       "2191           7.0          6.0   5.0    1.0             7.0              7.0   \n",
       "2192           5.0          4.0   2.0    0.0             NaN              NaN   \n",
       "2193           5.0          4.0   2.0    0.0             NaN              NaN   \n",
       "2194           3.0          4.0   5.0    1.0             6.0              5.0   \n",
       "2195           3.0          4.0   5.0    1.0             6.0              5.0   \n",
       "\n",
       "      AI_Contribution  DAT_Group  Group_  \n",
       "0                 NaN          3       1  \n",
       "1                 NaN          3       1  \n",
       "2                70.0          3       1  \n",
       "3                70.0          3       1  \n",
       "4                 NaN          3       2  \n",
       "...               ...        ...     ...  \n",
       "2191             91.0          3       3  \n",
       "2192              NaN          2       1  \n",
       "2193              NaN          2       1  \n",
       "2194             71.0          2       1  \n",
       "2195             71.0          2       1  \n",
       "\n",
       "[2196 rows x 30 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.transforms as transforms\n",
    "import seaborn as sns\n",
    "from IPython.display import set_matplotlib_formats\n",
    "%matplotlib inline\n",
    "set_matplotlib_formats('retina')\n",
    "from scipy import stats\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "import warnings\n",
    "import matplotlib.path as mpath\n",
    "sns.set(rc={\"figure.dpi\":100, 'savefig.dpi':300})\n",
    "sns.set_context('notebook')\n",
    "sns.set_style(\"ticks\")\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', None)\n",
    "df = pd.read_csv('Replication Data for Designing Human and Generative AI Collaboration.csv').iloc[:,1:]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09bfcbd",
   "metadata": {},
   "source": [
    "# A. Satisfaction with the Writing Process Across Groups and Days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29e876c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1072.38x300 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 334,
       "width": 871
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp3 = df.groupby(by=['ID', 'Day'])[['Group', 'Satisfaction', 'Flexibility', 'Goal', 'Again', 'AI_Helpfulness', 'AI_Satisfaction', 'AI_Contribution']].first().reset_index()\n",
    "tmp4 = tmp3.iloc[:,:7]\n",
    "tmp4['Again'] = tmp4['Again'] * 100\n",
    "tmp4 = tmp4[tmp4.Day==2]\n",
    "tmp5 = pd.melt(tmp4, id_vars=[\"ID\", 'Group', 'Day'], var_name='Category', value_name=\"Score\")\n",
    "tmp5.Day = tmp5.Day.astype('str')\n",
    "tmp5.Day.replace({'1':'Writing without AI', '2':'Writing with AI'}, inplace=True)\n",
    "tmp6 = tmp3.drop(columns=['Satisfaction', 'Flexibility', 'Goal', 'Again'])\n",
    "tmp6['AI_Contribution'] = tmp6['AI_Contribution']\n",
    "tmp6 = tmp6[tmp6.Day==2]\n",
    "tmp7 = pd.melt(tmp6, id_vars=[\"ID\", 'Group', 'Day'], var_name='Category', value_name=\"Score\")\n",
    "tmp7.Day = tmp7.Day.astype('str')\n",
    "tmp7.Day.replace({'1':'Writing without AI', '2':'Writing with AI'}, inplace=True)\n",
    "tmp8 = tmp3.iloc[:,:7]\n",
    "tmp8['Again'] = tmp8['Again'] * 100\n",
    "tmp9 = pd.melt(tmp8, id_vars=[\"ID\", 'Group', 'Day'], var_name='Category', value_name=\"Score\")\n",
    "tmp9.Day = tmp9.Day.astype('str')\n",
    "tmp9.Day.replace({'1':'Writing without AI', '2':'Writing with AI'}, inplace=True)\n",
    "tmp9.Category.replace({'Satisfaction':'Overall\\nSatisfaction', 'Goal':'Process\\nEffectiveness'}, inplace=True)\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming 'tmp9' DataFrame is already defined and contains the necessary data\n",
    "\n",
    "# Create the catplot\n",
    "ax = sns.catplot(\n",
    "    kind='bar',\n",
    "    data=tmp9[tmp9.Category != 'Again'],\n",
    "    x='Group',\n",
    "    y='Score',\n",
    "    hue='Day',\n",
    "    col='Category',\n",
    "    palette=sns.color_palette(['gray', 'white']),\n",
    "    edgecolor=\"k\",\n",
    "    width=0.8,\n",
    "    height=3,\n",
    "    aspect=1,\n",
    "    order=['Human Confirmation', 'Human Creativity', 'Copilot'],\n",
    "    errorbar=('ci', 95),\n",
    "    capsize=0.05,\n",
    "    errwidth=0.2\n",
    ")\n",
    "\n",
    "# Remove axis labels\n",
    "ax.set(xlabel='', ylabel='')\n",
    "\n",
    "# Set custom x-tick labels\n",
    "plt.xticks([0, 1, 2], ['Human\\nConfirmation', 'Human\\nCreativity', 'Copilot'])\n",
    "\n",
    "# Move the legend, remove its title, and arrange items side by side\n",
    "sns.move_legend(\n",
    "    ax,\n",
    "    \"upper center\",\n",
    "    bbox_to_anchor=(0.6, 1.15),\n",
    "    fontsize=12,\n",
    "    ncol=2  # Arrange legend items side by side\n",
    ")\n",
    "ax._legend.set_title('')  # Remove the legend title\n",
    "\n",
    "# Set the titles of the facets\n",
    "ax.set_titles(\"{col_name}\", size=15)\n",
    "\n",
    "# Annotate bars and adjust margins\n",
    "for axis in ax.axes.ravel():\n",
    "    for c in axis.containers:\n",
    "        axis.bar_label(c, label_type='edge', fmt='{:,.1f}', padding=5)\n",
    "    axis.margins(y=0.2)\n",
    "\n",
    "# Set y-axis limits\n",
    "ax.set(ylim=(3.5, 6))\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "ax.figure.savefig(\"Fig 3A - Satisfaction with the Writing Process Across Groups and Days (1).pdf\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79000f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 562.375x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 343
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mpl.rcParams['figure.figsize'] = (2,5)\n",
    "ax = sns.catplot(kind='bar', data=tmp9[tmp9.Category == 'Again'], x='Group', y='Score', hue='Day', col='Category',\n",
    "                 palette=sns.color_palette(['gray', 'white']), edgecolor=\"k\", width=0.75, height=3, aspect=1.3,\n",
    "                 order = ['Human Confirmation', 'Human Creativity', 'Copilot'],\n",
    "                 errorbar = ('ci', 95), capsize=0.1, errwidth=0.5)\n",
    "ax.set(xlabel='', ylabel='')\n",
    "plt.xticks([0, 1, 2], ['Human\\nConfirmation', 'Human\\nCreativity',  'Copilopt'])\n",
    "#sns.move_legend(ax, \"upper left\", bbox_to_anchor=(0.7, 1.04), fontsize=12)\n",
    "ax.legend.remove()\n",
    "ax.set_titles(\"Reuse Again\", size=15)\n",
    "# Move the legend, remove its title, and arrange items side by side\n",
    "\n",
    "for ax in ax.axes.ravel():\n",
    "    # add annotations\n",
    "    for c in ax.containers:\n",
    "        # add custom labels with the labels=labels parameter if needed\n",
    "        # labels = [f'{h}' if (h := v.get_height()) > 0 else '' for v in c]\n",
    "        ax.bar_label(c, label_type='edge', fmt='{:,.0f}%', padding=5)\n",
    "    ax.margins(y=0.2)\n",
    "\n",
    "ax.set_ylim(0,100)\n",
    "ax.figure.savefig(\"Fig 3A - Satisfaction with the Writing Process Across Groups and Days (2).pdf\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79547711",
   "metadata": {},
   "source": [
    "# B. Satisfaction with AI Assistance Across Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e9eb0ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x250 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 245,
       "width": 429
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.figsize'] = (5,2.5)\n",
    "ax = sns.barplot(x=\"Category\", y=\"Score\", data=tmp7[tmp7.Category != 'AI_Contribution'], hue='Group',\n",
    "                 hue_order = ['Human Confirmation', 'Human Creativity', 'Copilot'],\n",
    "                 width=0.6, errorbar = ('ci', 95), capsize=0.05, errwidth=0.2,\n",
    "                 palette=sns.color_palette(['darkgray', 'lightgray', 'white']), edgecolor=\"gray\")\n",
    "ax.set(xlabel='', ylabel='')\n",
    "for c in ax.containers:\n",
    "    ax.bar_label(c, fmt='{:,.1f}', padding=5)\n",
    "ax.legend_.remove()\n",
    "#sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1.04))\n",
    "plt.xticks([0, 1], ['Effectiveness of AI', 'Satisfaction with AI'])\n",
    "plt.ylim(3.5,7)\n",
    "ax.figure.savefig(\"Fig 3B - Satisfaction with AI Assistance Across Groups (1).pdf\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea4cc2b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x250 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 245,
       "width": 494
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.figsize'] = (3,2.5)\n",
    "ax = sns.barplot(x=\"Category\", y=\"Score\", data=tmp7[tmp7.Category == 'AI_Contribution'], hue='Group',\n",
    "                 hue_order = ['Human Confirmation', 'Human Creativity', 'Copilot'],\n",
    "                 width=0.6, errorbar = ('ci', 95), capsize=0.05, errwidth=0.2,\n",
    "                 palette=sns.color_palette(['darkgray', 'lightgray', 'white']), edgecolor=\"gray\")\n",
    "ax.set(xlabel='', ylabel='')\n",
    "for c in ax.containers:\n",
    "    ax.bar_label(c, fmt='{:,.0f}%', padding=5)\n",
    "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1.04))\n",
    "plt.xticks([0], ['Contribution of AI'])\n",
    "plt.ylim(40,100)\n",
    "ax.figure.savefig(\"Fig 3B - Satisfaction with AI Assistance Across Groups (2).pdf\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbc42dab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
